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Enhancing Targeted Attack Transferability via Diversified Weight Pruning

Enhancing Targeted Attack Transferability via Diversified We...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hung-Jui Wang Yu-Yu Wu Shang-Tse Chen National Taiwan University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Malicious attackers generate adversarial instances by introducing imperceptible perturbations into data. Even in the black-box setting where model details are concealed, attackers still exploit networks with cross-model transferability. Despite the notable success of untargeted attacks, achieving targeted attack transferability remains a challenging endeavor. Recent investigations have demonstrated the efficacy of ensemble-based techniques. However, utilizing additional models to carry out ensemble attacks brings extra costs. To reduce the number of white-box models required, model augmentation methods augment the given network to produce a variant of diverse models, contributing useful gradients for attack. In this work, we propose Diversified Weight Pruning (DWP) as an innovative model augmentation technique specifically designed to facilitate the generation of transferable targeted attacks. In contrast to prior techniques, DWP preserves essential connections while simultaneously ensuring diversity among the pruned models, both of which are identified as pivotal factors for targeted transferability. DWP is shown effective with experiments on ImageNet under challenging conditions, with enhancements of up to 10.1%, 6.6%, and 7.0% across adversarially trained models, Non-CNN architectures, and Google Cloud vision respectively.

关键词： computer vision Image coding Costs Perturbation methods conferences computer architecture pattern recognition

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Multi-View Action recognition for Distracted Driver Behavior Localization

Multi-View Action Recognition for Distracted Driver Behavior...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yuehuan Xu Shuai Jiang Zhe Cui Fei Su Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System and Network Culture China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The detection and recognition of distracted driving behaviors has emerged as a new vision task with the rapid development of computer vision, which is considered as a challenging temporal action localization (TAL) problem in computer vision. The primary goal of temporal localization is to determine the start and end time of actions in untrimmed videos. Currently, most state-of-the-art temporal localization methods adopt complex architectures, which are cumbersome and time-consuming. In this paper, we propose a robust and efficient two-stage framework for distracted behavior classification-localization based on the sliding window approach, which is suitable for untrimmed naturalistic driving videos. To address the issues of high similarity among different behaviors and interference from background classes, we propose a multi-view fusion and adaptive thresholding algorithm, which effectively reduces missing detections. To address the problem of fuzzy behavior boundary localization, we design a post-processing procedure that achieves fine localization from coarse localization through post connection and candidate behavior merging criteria. In the AICITY2024 Task3 TestA, our method performs well, achieving Average Intersection over Union(AIOU) of 0.6080 and ranking eighth in AICITY2024 Task3. Our code will be released in the near future.

关键词： Location awareness computer vision Filtering Splicing Urban areas Merging Interference

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Towards Online Real-Time Memory-based Video Inpainting Transformers

Towards Online Real-Time Memory-based Video Inpainting Trans...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Guillaume Thiry Hao Tang Radu Timofte Luc Van Gool CVL ETH Zurich Carnegie Mellon University University of Wurzburg ESAT-PSI KU Leuven INSAIT Sofia Un. St. Kliment Ohridski

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video inpainting tasks have seen significant improvements in recent years with the rise of deep neural networks and, in particular, vision transformers. Although these models show promising reconstruction quality and temporal consistency, they are still unsuitable for live videos, one of the last steps to make them completely convincing and usable. The main limitations are that these state-of-the-art models inpaint using the whole video (offline processing) and show an insufficient frame rate. In our approach, we propose a framework to adapt existing inpainting transformers to these constraints by memorizing and refining redundant computations while maintaining a decent inpainting quality. Using this framework with some of the most recent inpainting models, we show great online results with a consistent throughput above 20 frames per second.

关键词： computer vision Adaptation models Computational modeling conferences Refining Streaming media Transformers

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Difficulty Estimation with Action Scores for computer vision Tasks

Difficulty Estimation with Action Scores for Computer Vision...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Octavio Arriaga Sebastian Palacio Matias Valdenegro-Toro University of Bremen German Research Center for Artificial Intelligence University of Groningen

As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficulty score based on the accumulated loss per epoch. Our proposed method does not require any modification to the model, neither any external supervision, and it can be easily applied to a wide range of machine learning tasks. We provide results for the tasks of image classification, image segmentation, and object detection. We compare our score against similar metrics and provide theoretical and empirical evidence of their difference. Furthermore, we show applications of our proposed score for detecting incorrect labels, and test for possible biases.

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Quality-based Artifact Modeling for Facial Deepfake Detection in Videos

Quality-based Artifact Modeling for Facial Deepfake Detectio...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sara Concas Simone Maurizio La Cava Roberto Casula Giulia Orrù Giovanni Puglisi Gian Luca Marcialis University of Cagliari Cagliari Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Facial deepfakes are becoming more and more realistic, to the point that it is often difficult for humans to distinguish between a fake and a real video. However, it is acknowledged that deepfakes contain artifacts at different levels; we hypothesize a connection between manipulations and visible or non-visible artifacts, especially where the subject’s movements are difficult to reproduce in detail. Accordingly, our approach relies on different quality measures, No-Reference (NR) and Full-Reference (FR), over the detected faces in the video. The measurements allow us to adopt a frame-by-frame approach to build an effective matrix-based representation of a video sequence. We show that the results obtained by this basic feature set for a neural network architecture constitute the first step that encourages the empowerment of this representation, aimed to extend our investigation to further deepfake classes. The FaceForensics++ dataset is chosen for experiments, which allows the evaluation of the proposed approach over different deepfake generation algorithms.

关键词： Deepfakes computer vision conferences Video sequences Neural networks computer architecture Benchmark testing

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HirFormer: Dynamic High Resolution Transformer for Large-Scale Image Shadow Removal

HirFormer: Dynamic High Resolution Transformer for Large-Sca...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xin Lu Yurui Zhu Xi Wang Dong Li Jie Xiao Yunpeng Zhang Xueyang Fu Zheng-Jun Zha University of Science and Technology of China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing image restoration models have limited performance in high-resolution image shadow removal tasks, particularly in handling complex background information and unevenly distributed shadows. To address this challenge, we propose a novel two-stage approach called HirFormer for high-resolution image shadow removal. The first stage, Dynamic High Resolution Transformer, reconstructs the high-resolution background information and removes a significant portion of the shadows based on the Transformer architecture. The second stage, Large-scale Image Refinement, incorporates the NAFNet model to further eliminate residual shadows and address block artifacts introduced by the first stage. Experimental results on official datasets validate the superiority of our method compared to existing approaches, and our approach emerged as the winner in the fidelity track of the NTIRE 2024 Shadow Removal Challenge during the final testing competition (1st place).

关键词： Measurement computer vision Image resolution conferences computer architecture Transformers Image restoration

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Class-Incremental Mixture of Gaussians for Deep Continual Learning

Class-Incremental Mixture of Gaussians for Deep Continual Le...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Lukasz Korycki Bartosz Krawczyk Virginia Commonwealth University Rochester Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Continual learning models for stationary data focus on learning and retaining concepts coming to them in a sequential manner. In the most generic class-incremental environment, we have to be ready to deal with classes coming one by one, without any higher-level grouping. This requirement invalidates many previously proposed methods and forces researchers to look for more flexible alternative approaches. In this work, we follow the idea of centroid-driven methods and propose end-to-end incorporation of the mixture of Gaussians model into the continual learning framework. By employing the gradient-based approach and designing losses capable of learning discriminative features while avoiding degenerate solutions, we successfully combine the mixture model with a deep feature extractor allowing for joint optimization and adjustments in the latent space. Additionally, we show that our model can effectively learn in memory-free scenarios with fixed extractors. In the conducted experiments, we empirically demonstrate the effectiveness of the proposed solutions and exhibit the competitiveness of our model when compared with state-of-the-art continual learning baselines evaluated in the context of image classification problems.

关键词： Continuing education computer vision conferences Mixture models Feature extraction Data models pattern recognition

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Contextualising Implicit Representations for Semantic Tasks

Contextualising Implicit Representations for Semantic Tasks

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Theo W. Costain Kejie Li Victor A. Prisacariu Active Vision Lab University of Oxford

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Prior works have demonstrated that implicit representations trained only for reconstruction tasks typically generate encodings that are not useful for semantic tasks. In this work, we propose a method that contextualises the encodings of implicit representations, enabling their use in downstream tasks (e.g. semantic segmentation), without requiring access to the original training data or encoding network. Using an implicit representation trained for a reconstruction task alone, our contextualising module takes an encoding trained for reconstruction only and reveals meaningful semantic information that is hidden in the encodings, without compromising the reconstruction performance. With our proposed module, it becomes possible to pre-train implicit representations on larger datasets, improving their reconstruction performance compared to training on only a smaller labelled dataset, whilst maintaining their segmentation performance on the labelled dataset. Importantly, our method allows for future foundation implicit representation models to be fine-tuned on unseen tasks, without retraining the encoder.

关键词： Training computer vision Semantic segmentation conferences Semantics Training data Encoding

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Face Parsing from RGB and Depth Using Cross-Domain Mutual Learning

Face Parsing from RGB and Depth Using Cross-Domain Mutual Le...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lee, Jihyun Bhattarai, Binod Kim, Tae-Kyun Korea Adv Inst Sci & Technol Seoul South Korea Imperial Coll London London England

ISBN: (纸本)9781665448994

Existing methods of face parsing have proven effective at classifying each pixel of an RGB image into different facial components. However, there is a lack of face parsing research that utilizes depth domain. To the best of our knowledge, we present the first study to exploit 2.5D data for face parsing. We introduce a novel framework to jointly learn (1) RGB face parsing, (2) depth face parsing and (3) RGB-to-depth domain translation, which can be effective even when only a small amount of annotated depth data is available for training. To this end, we also create the first RGB-D face parsing benchmarks based on CelebAMask-HQ, LaPa and Helen by utilizing an off-the-shelf 3D head reconstruction model. Overall, our approach makes two main contributions. First, our method leverages mutual learning between RGB and depth face parsing, which enables bidirectional knowledge distillation between the two data domains. Second, our method utilizes end-to-end learning of RGB-to-depth domain translation and depth face parsing, which can help overcome the scarcity of annotated depth data. We perform extensive experiments to validate the effectiveness of our method, in which we achieve state-of-the-art results in RGB face parsing. As far as we know, we also report the first results on face parsing from depth data. All experiments are conducted on our new RGB-D face parsing datasets, which are publicly available at https://***/jyunlee/CelebAMaskHQ-D_LaPa-D_Helen-D.

关键词： Training Solid modeling computer vision Three-dimensional displays Head Face recognition conferences

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NTIRE 2024 Challenge on Night Photography Rendering

NTIRE 2024 Challenge on Night Photography Rendering

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Egor Ershov Artyom Panshin Oleg Karasev Sergey Korchagin Shepelev Lev Alexandr Startsev Daniil Vladimirov Ekaterina Zaychenkova Nikola Banić Dmitrii R Iarchuk Maria Efimova Radu Timofte Arseniy Terekhin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year’s competition, the challenge images were collected with a mobile phone and the speed of algorithms was also measured alongside the quality of their output. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. There were 2 nominations: quality and efficiency. Top 5 solutions in terms of output quality were sorted by evaluation time (see Fig. 1). The top ranking participants’ solutions effectively represent the state-of-the-art in night-time photography rendering. More results can be found at https://***.

关键词： Photography Visualization computer vision Reviews conferences Rendering (computer graphics) Cameras

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